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d7dde45 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | import pandas as pd
import os
from sklearn.model_selection import train_test_split
def prepare_data(input_csv_path='engine1/data/engine.csv', output_dir='engine1/data'):
# 1. Load the dataset
df = pd.read_csv(input_csv_path)
# 2. Define column renaming mapping
column_name_mapping = {
'Engine rpm': 'engine_rpm',
'Lub oil pressure': 'lub_oil_pressure',
'Fuel pressure': 'fuel_pressure',
'Coolant pressure': 'coolant_pressure',
'lub oil temp': 'lub_oil_temp',
'Coolant temp': 'coolant_temp',
'Engine Condition': 'engine_condition'
}
# 3. Rename columns
df.rename(columns=column_name_mapping, inplace=True)
# 4. Separate features (X) and target (y)
X = df.drop('engine_condition', axis=1)
y = df['engine_condition']
# 5. Split data into training and testing sets with stratification
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
# 6. Create output directory if it doesn't exist
os.makedirs(output_dir, exist_ok=True)
# 7. Save prepared datasets
X_train.to_csv(os.path.join(output_dir, 'X_train.csv'), index=False)
X_test.to_csv(os.path.join(output_dir, 'X_test.csv'), index=False)
y_train.to_csv(os.path.join(output_dir, 'y_train.csv'), index=False)
y_test.to_csv(os.path.join(output_dir, 'y_test.csv'), index=False)
print(f"Data preparation complete. Saved files to {output_dir}")
# Example usage (can be called from another script or notebook)
# if __name__ == '__main__':
# prepare_data()
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